A fast nonlinear model identification method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem r...

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Bibliographic Details
Published in:IEEE transactions on automatic control Vol. 50; no. 8; pp. 1211 - 1216
Main Authors: Kang Li, Jian-Xun Peng, Irwin, G.W.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:0018-9286, 1558-2523
Online Access:Get full text
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Summary:The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2005.852557